Presentation + Paper
1 March 2018 Improved identification of cranial nerves using paired-agent imaging: topical staining protocol optimization through experimentation and simulation
Veronica C. Torres, Todd Wilson, Austeja Staneviciute, Richard W. Byrne, Kenneth M. Tichauer
Author Affiliations +
Abstract
Skull base tumors are particularly difficult to visualize and access for surgeons because of the crowded environment and close proximity of vital structures, such as cranial nerves. As a result, accidental nerve damage is a significant concern and the likelihood of tumor recurrence is increased because of more conservative resections that attempt to avoid injuring these structures. In this study, a paired-agent imaging method with direct administration of fluorophores is applied to enhance cranial nerve identification. Here, a control imaging agent (ICG) accounts for non-specific uptake of the nerve-targeting agent (Oxazine 4), and ratiometric data analysis is employed to approximate binding potential (BP, a surrogate of targeted biomolecule concentration). For clinical relevance, animal experiments and simulations were conducted to identify parameters for an optimized stain and rinse protocol using the developed paired-agent method. Numerical methods were used to model the diffusive and kinetic behavior of the imaging agents in tissue, and simulation results revealed that there are various combinations of stain time and rinse number that provide improved contrast of cranial nerves, as suggested by optimal measures of BP and contrast-to-noise ratio.
Conference Presentation
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Veronica C. Torres, Todd Wilson, Austeja Staneviciute, Richard W. Byrne, and Kenneth M. Tichauer "Improved identification of cranial nerves using paired-agent imaging: topical staining protocol optimization through experimentation and simulation ", Proc. SPIE 10478, Molecular-Guided Surgery: Molecules, Devices, and Applications IV, 104780I (1 March 2018); https://doi.org/10.1117/12.2285206
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KEYWORDS
Diffusion

Tissues

Nerve

Brain

Data modeling

Surgery

Computer simulations

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